




'''simple docstring'''


# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]					,       a_						:				List[Any]					,       a_						:				List[str]					,       a_						:				List[Any]      ):
  __a								=       multiprocessing.Manager()
  __a								=       manager.list()

  __a								=       multiprocessing.Process(target=a_					,       args=(check_program, result, timeout)      )
  p.start()
  p.join(timeout=timeout + 1      )
  if p.is_alive():
    p.kill()

  if not result:
    result.append('timed out'      )

  return {
      "task_id": task_id,
      "passed": result[0] == "passed",
      "result": result[0],
      "completion_id": completion_id,
  }


def     SCREAMING_SNAKE_CASE							(   a_						:				Dict					,       a_						:				str					,       a_						:				int      ):
  with create_tempdir():
    # These system calls are needed when cleaning up tempdir.
    import os
    import shutil

    __a								=       shutil.rmtree
    __a								=       os.rmdir
    __a								=       os.chdir

    # Disable functionalities that can make destructive changes to the test.
    reliability_guard()

    # Run program.
    try:
      __a								=       {}
      with swallow_io():
        with time_limit(a_      ):
          exec(a_					,       a_      )
      result.append('passed'      )
    except TimeoutException:
      result.append('timed out'      )
    except BaseException as e:
      result.append(f"failed: {e}"      )

    # Needed for cleaning up.
    __a								=       rmtree
    __a								=       rmdir
    __a								=       chdir


@contextlib.contextmanager
def     SCREAMING_SNAKE_CASE							(   a_						:				str      ):
  def signal_handler(a_						:				List[str]					,       a_						:				Tuple      ):
    raise TimeoutException('Timed out!'      )

  signal.setitimer(signal.ITIMER_REAL					,       a_      )
  signal.signal(signal.SIGALRM					,       a_      )
  try:
    yield
  finally:
    signal.setitimer(signal.ITIMER_REAL					,       0      )


@contextlib.contextmanager
def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       WriteOnlyStringIO()
  with contextlib.redirect_stdout(a_      ):
    with contextlib.redirect_stderr(a_      ):
      with redirect_stdin(a_      ):
        yield


@contextlib.contextmanager
def     SCREAMING_SNAKE_CASE							(   ):
  with tempfile.TemporaryDirectory() as dirname:
    with chdir(a_      ):
      yield dirname





class  __lowercase    (	__magic_name__			):
   pass





class  __lowercase    (	io.StringIO			):







   def 		UpperCamelCase__      (						self   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     List[Any]:
     raise OSError







   def 		UpperCamelCase__      (						self   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     Dict:
     raise OSError







   def 		UpperCamelCase__      (						self   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     Any:
     raise OSError







   def 		UpperCamelCase__      (						self   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     Tuple:
     return False





class  __lowercase    (	contextlib._RedirectStream			):  # type: ignore
   _a    						=   """stdin"""


@contextlib.contextmanager
def     SCREAMING_SNAKE_CASE							(   a_						:				str      ):
  if root == ".":
    yield
    return
  __a								=       os.getcwd()
  os.chdir(a_      )
  try:
    yield
  except BaseException as exc:
    raise exc
  finally:
    os.chdir(a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]=None      ):

  if maximum_memory_bytes is not None:
    import resource

    resource.setrlimit(resource.RLIMIT_AS					,       (maximum_memory_bytes, maximum_memory_bytes)      )
    resource.setrlimit(resource.RLIMIT_DATA					,       (maximum_memory_bytes, maximum_memory_bytes)      )
    if not platform.uname().system == "Darwin":
      resource.setrlimit(resource.RLIMIT_STACK					,       (maximum_memory_bytes, maximum_memory_bytes)      )

  faulthandler.disable()

  import builtins

  __a								=       None
  __a								=       None

  import os

  __a								=       '1'

  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None

  import shutil

  __a								=       None
  __a								=       None
  __a								=       None

  import subprocess

  __a								=       None  # type: ignore

  __a								=       None

  import sys

  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None
  __a								=       None









'''simple docstring'''


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/blenderbot_small-90M": 5_12,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   BlenderbotSmallTokenizer







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     List[Any]:
     super().__init__(
         ByteLevelBPETokenizer(
             vocab=UpperCamelCase   ,				merges=UpperCamelCase   ,				add_prefix_space=UpperCamelCase   ,				trim_offsets=UpperCamelCase   ,				)   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       add_prefix_space







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[Any]:
     __a								=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



